Prediction method, device and equipment for volcanic rock facies and storage medium

Abstract

The invention provides a volcanic rock facies prediction method, device and equipment and a storage medium, and belongs to the field of oil exploration and development geophysics. The volcanic rock lithofacies prediction method comprises the following steps: acquiring volcanic rock logging curve data and seismic data; the logging curve data comprises interval transit time curve data, gamma curve data and density curve data, and the seismic data comprises post-stack seismic data; fitting the interval transit time curve data and the gamma curve data to obtain lithofacies indication curve data; according to the interval transit time curve data and the density curve data, performing waveform indication inversion on the post-stack seismic data to obtain an acoustic wave impedance body; based on the constraint of the sound wave impedance body, waveform indication simulation is carried out on the lithofacies indication curve data and the post-stack seismic data, and finally a volcanic rock lithofacies prediction data body is obtained. The precision and accuracy of volcanic rock facies prediction are improved, the problem of complicated volcanic rock facies zone distribution recognition is solved, and exploration and development of volcanic rock oil and gas reservoirs are guided.

Description

Technical Field

[0001] The present invention relates to the field of geophysics in petroleum exploration and development, and in particular to a method for predicting volcanic rock facies, a device for predicting volcanic rock facies, an electronic device and a readable storage medium. Background Art

[0002] With the gradual deepening of oil and gas exploration in unconventional reservoirs, volcanic rocks have gradually become a new research hotspot for reservoir prediction. However, the prediction of volcanic rock lithofacies is affected due to the complex lithology of volcanic reservoirs, the complex and diverse reservoir space, and the large variations in reservoir porosity and permeability.

[0003] Conventional acoustic impedance cannot well identify the changes in volcanic rock phases. Therefore, the existing technology proposes to establish a volcanic rock lithology sequence in the coring section through the well, summarize the basic characteristics of logging responses of different volcanic rocks, and establish a BP neural network lithology prediction model to predict the volcanic rock lithology. However, methods based on neural networks or machine learning require many sample points. If there are few sample points, the prediction results will be inaccurate, and they are only suitable for the oil and gas field development stage. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a method, device, equipment and storage medium for predicting volcanic rock facies, so as to solve the problem of low accuracy in predicting volcanic rock facies.

[0005] In order to achieve the above object, the embodiment of the present invention provides a method for predicting volcanic rock facies, comprising:

[0006] Acquire volcanic rock logging curve data and seismic data; wherein the logging curve data includes acoustic wave time difference curve data, gamma curve data and density curve data, and the seismic data includes post-stack seismic data;

[0007] Fitting the acoustic time difference curve data and the gamma curve data to obtain lithofacies indication curve data;

[0008] According to the acoustic wave time difference curve data and the density curve data, waveform indication inversion is performed on the post-stack seismic data to obtain an acoustic wave impedance body;

[0009] Based on the constraint of the acoustic wave impedance body, waveform indication simulation is performed on the lithofacies indication curve data and the post-stack seismic data to obtain a predicted volcanic rock lithofacies data body.

[0010] Optionally, fitting the acoustic time difference curve data and the gamma curve data to obtain the lithofacies indicator curve data includes:

[0011] The lithofacies indication curve data is obtained according to the acoustic time difference curve data, the gamma curve data and the formula 1; wherein the formula 1 includes:

[0012] litho=AC / GR,

[0013] Among them, litho is the lithofacies indication curve data, AC is the acoustic wave time difference curve data, and GR is the gamma curve data.

[0014] Optionally, before performing waveform indication inversion on the post-stack seismic data according to the acoustic wave time difference curve data and the density curve data to obtain an acoustic wave impedance body, the method further comprises:

[0015] Calculating an acoustic wave impedance curve according to the acoustic wave time difference curve data and the density curve data;

[0016] According to the acoustic wave impedance curve, obtaining a reflection coefficient of the acoustic wave impedance curve;

[0017] Synthetic seismic record data is obtained according to the reflection coefficient and the seismic data; wherein the seismic data includes seismic wavelet data.

[0018] Optionally, the synthesized seismic record data is obtained according to the reflection coefficient and the seismic data; wherein the seismic data includes seismic wavelet data, including:

[0019] According to the reflection coefficient, the seismic wavelet data and the formula 2, synthetic seismic record data is obtained; wherein the formula 2 includes:

[0020] s(t)=r(t)*w(t),

[0021] Wherein, s(t) is the synthetic seismic record data, r(t) is the reflection coefficient, w(t) is the seismic wavelet data, and * is the convolution calculation.

[0022] Optionally, after obtaining synthetic seismic record data according to the reflection coefficient and the seismic data, the method further comprises:

[0023] Determining whether the synthetic seismic record data is consistent with the time domain waveform of the post-stack earthquake;

[0024] If not, the process returns to executing the step of calculating the acoustic wave impedance curve based on the acoustic wave time difference curve data and the density curve data.

[0025] Optionally, after determining whether the synthetic seismic record data is consistent with the time domain waveform of the post-stack seismic, the method further comprises:

[0026] If yes, then perform the waveform indication inversion on the post-stack seismic data according to the acoustic wave time difference curve data and the density curve data to obtain an acoustic wave impedance body;

[0027] The method of performing waveform indication inversion on the post-stack seismic data according to the acoustic wave time difference curve data and the density curve data to obtain an acoustic wave impedance body comprises:

[0028] If so, waveform inversion is performed on the post-stack seismic data according to the synthetic seismic record data to obtain the acoustic wave impedance body.

[0029] Optionally, based on the acoustic wave impedance body, the waveform indication simulation is performed on the lithofacies indication curve data to obtain a predicted volcanic rock lithofacies data body, including:

[0030] According to the acoustic wave impedance body, a low-frequency model based on the acoustic wave impedance body is obtained;

[0031] According to the low-frequency model and the seismic data, waveform indication simulation is performed on the lithofacies indication curve data to obtain the predicted volcanic rock lithofacies data.

[0032] In a second aspect of the embodiments of the present invention, a device for predicting volcanic rock facies is provided, comprising:

[0033] An acquisition module, used to acquire volcanic rock logging curve data and seismic data; wherein the logging curve data includes acoustic wave time difference curve data, gamma curve data and density curve data, and the seismic data includes post-stack seismic data;

[0034] A fitting module, used for fitting the acoustic time difference curve data and the gamma curve data to obtain lithofacies indication curve data;

[0035] An inversion module, used for performing waveform indication inversion on the post-stack seismic data according to the acoustic wave time difference curve data and the density curve data to obtain an acoustic wave impedance body;

[0036] The prediction module is used to perform waveform indication simulation on the lithofacies indication curve data and the post-stack seismic data based on the constraints of the acoustic wave impedance body to obtain a predicted volcanic rock lithofacies data body.

[0037] In a third aspect of the embodiments of the present invention, an electronic device is provided, comprising: a processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the steps described in any possible implementation of the first aspect are performed.

[0038] In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, on which instructions are stored, and the instructions are used to enable a machine to execute the various steps described in any possible implementation of the first aspect.

[0039] The embodiment of the present invention obtains well logging curve data and seismic data of volcanic rocks, wherein the well logging curve data includes acoustic wave time difference curve data, gamma curve data and density curve data, and the seismic data includes post-stack seismic data, fits the acoustic wave time difference curve data and the gamma curve data to obtain lithofacies indication curve data, performs waveform indication inversion on the post-stack seismic data according to the acoustic wave time difference data and the density curve data to obtain an acoustic wave impedance body, and then based on the acoustic wave impedance body, further performs waveform indication simulation on the lithofacies indication curve data to obtain a predicted volcanic rock lithofacies data body.

[0040] The present invention fits a volcanic rock lithofacies indication curve and performs waveform indication simulation with seismic data, that is, matches and learns the logging phase and the seismic phase, and applies the waveform indication method to establish a relationship between the two, so as to predict the lateral distribution of the volcanic rock lithofacies, thereby solving the problem of low accuracy of volcanic rock lithofacies prediction in the prior art and improving the precision and accuracy of volcanic rock lithofacies prediction.

[0041] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0043] Figure 1 1 is a schematic flow chart of the first embodiment of the method for predicting volcanic rock facies of the present invention;

[0044] Figure 2 It is the well logging curve and lithophase comparison diagram;

[0045] Figure 3 It is a schematic diagram of well seismic calibration;

[0046] Figure 4 is a schematic diagram of the wave impedance inversion profile;

[0047] Figure 5 It is a schematic diagram of the predicted volcanic rock facies section;

[0048] Figure 6 It is the predicted plane map of volcanic rocks in the eruptive phase;

[0049] Figure 7It is a schematic diagram of the functional modules of the volcanic rock lithofacies prediction method device involved in the present application. DETAILED DESCRIPTION

[0050] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0052] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0053] Embodiment 1

[0054] Please refer to Figure 1 , Figure 1 It is a schematic flow chart of a method for predicting volcanic rock facies provided in this embodiment.

[0055] Step S100: Acquire volcanic rock logging curve data and seismic data; wherein the logging curve data includes acoustic wave time difference curve data, gamma curve data and density curve data, and the seismic data includes post-stack seismic data.

[0056] Well logging curve data is a well logging curve composed of well logging data obtained after the well logging operation. The well logging curves obtained by different well logging methods are different. For example, the above-mentioned acoustic time difference curve data is the well logging curve data obtained by measuring the acoustic wave velocity of the formation; the above-mentioned gamma curve data is the well logging curve data obtained by measuring the intensity of gamma rays emitted during the decay of naturally existing radioactive nuclides in the rock formation in the well; the above-mentioned density curve data is the density well logging curve formed by using an isotope gamma ray source to radiate gamma rays to the formation, and then using a detector at a certain distance from the gamma source to measure the intensity of gamma rays that reach the detector after being scattered and absorbed by the formation.

[0057] Seismic data is data obtained using seismic exploration technology.

[0058] Post-stack seismic data is a two-dimensional or three-dimensional data volume formed by stacking seismic data after dynamic correction and static correction. The signal-to-noise ratio of post-stack seismic data is high.

[0059] Step S200: Fitting the acoustic time difference curve data and the gamma curve data to obtain the lithofacies indication curve data.

[0060] It is worth mentioning that the acoustic time difference curve data and the gamma curve data are fitted, specifically, Figure 2 As shown, the acoustic time difference curve and the gamma curve are divided by formula one to calculate the lithofacies indicator curve. The value range of the curve is 1-4, which roughly corresponds to the lithofacies interpretation result: volcanic sediments correspond to 1, eruptions correspond to 2, and overflows correspond to 4. Sedimentary rocks and volcanic sediments correspond to the same value of 2. This lithofacies indicator curve can distinguish the volcanic sedimentary phase, eruption phase and overflow phase. The above formula one is specifically:

[0061] litho=AC / GR,

[0062] Among them, litho is the lithofacies indicator curve data, AC is the acoustic time difference curve data, and GR is the gamma curve data.

[0063] Step S300: According to the acoustic wave time difference curve data and the density curve data, waveform indication inversion is performed on the post-stack seismic data to obtain an acoustic wave impedance body.

[0064] The acoustic impedance is the ratio of the pressure acting on a certain area to the mass flow rate (i.e., the area multiplied by the mass vibration velocity) passing through the area vertically per unit time when the seismic wave propagates in the medium. It has the meaning of resistance. The acoustic impedance in this embodiment is the acoustic impedance in the time domain, such as Figure 4 The acoustic impedance values ​​of volcanic rocks in different lithophases are different. The acoustic impedance values ​​of volcanic rocks in different lithophases are different. Among them, the acoustic impedance of volcanic rocks in the explosive phase is the lowest, and the acoustic impedance of volcanic rocks in the effusive phase is the highest.

[0065] Waveform indication inversion is a high-precision inversion method based on data learning. In a specific embodiment, waveform indication inversion of post-stack seismic data is performed by comparing and learning with other seismic traces based on a combination of a strong amplitude and a medium-strong amplitude of the post-stack seismic data. After multiple learning and comparisons, a reservoir similar to the combined waveform is obtained, and then the acoustic wave impedance body of the reservoir is obtained.

[0066] In a specific embodiment, before step S300, the method further includes the following steps:

[0067] Step S310: Calculate and obtain the acoustic wave impedance curve according to the acoustic wave time difference curve data and the density curve data.

[0068] The value of the acoustic impedance curve is equal to the product of the medium density and the wave velocity. The acoustic impedance curve can be calculated based on the acoustic time difference curve data and the density curve data, and the acoustic impedance curve is a depth domain curve.

[0069] Step S320: Obtaining a reflection coefficient of the acoustic impedance curve according to the acoustic impedance curve.

[0070] When an electromagnetic wave passes from a uniform medium with a magnetic permeability of μ1 and a dielectric constant of ε1 into another uniform medium with a magnetic permeability of μ2 and a dielectric constant of ε2, a portion of the electromagnetic wave is reflected back at the interface, and the ratio of the amplitude of the reflected wave to the amplitude of the incident wave is called the reflection coefficient. The reflection coefficient of this embodiment is the reflection coefficient corresponding to the acoustic wave impedance curve.

[0071] Step S330: obtaining synthetic seismic record data according to the reflection coefficient and the seismic data; wherein the seismic data includes seismic wavelet data.

[0072] Seismic wavelet data is a signal with a certain starting time, limited energy and a certain duration. It is the basic unit in seismic records.

[0073] It can be understood that the reflection coefficient and seismic wavelet data are convoluted using Formula 2 to obtain synthetic seismic record data, such as Figure 3 As shown, this step converts the logging curve in the depth domain into synthetic seismic record data in the time domain, so that the time domain data is linked to the depth domain data. Wherein, Formula 2 specifically includes:

[0074] s(t)=r(t)*w(t),

[0075] Among them, s(t) is the synthetic seismic record data, r(t) is the reflection coefficient, w(t) is the seismic wavelet data, and * is the convolution calculation.

[0076] Step S340: Determine whether the synthetic seismic record data is consistent with the time domain waveform of the post-stack earthquake.

[0077] Step S350: If not, return to execute the step of calculating the acoustic wave impedance curve based on the acoustic wave time difference curve data and the density curve data.

[0078] Step S360: If yes, then perform the waveform indication inversion on the post-stack seismic data according to the acoustic wave time difference curve data and the density curve data to obtain an acoustic wave impedance body.

[0079] It is understandable that if Figure 3As shown, it is determined whether the time domain waveforms of the synthetic seismic trace and the post-stack seismic trace are consistent. If they are consistent, it means that the operation of converting the logging data in the depth domain into the seismic data in the time domain is successful and can be continued; if they are inconsistent, it means that the conversion synthesis is unsuccessful and the acoustic wave impedance curve needs to be recalculated.

[0080] This embodiment calculates the acoustic wave impedance curve in the depth domain based on the acoustic wave time difference curve and the density curve data, and further obtains the reflection coefficient corresponding to the acoustic wave impedance curve. According to the reflection coefficient and the seismic wavelet data, synthetic seismic record data is obtained to determine whether the synthetic seismic record data is consistent with the waveform of the post-stack seismic data. If they are consistent, the synthetic seismic record in the time domain is further used to calculate the acoustic wave impedance body in the time domain. If not, it returns to recalculate the acoustic wave impedance curve. This embodiment realizes the establishment of a connection between the depth and the logging data and the seismic data in the time domain.

[0081] Step S400: Based on the constraints of the acoustic wave impedance body, waveform indication simulation is performed on the lithofacies indication curve data and the post-stack seismic data to obtain a predicted volcanic rock lithofacies data body.

[0082] The predicted volcanic rock facies data is the volcanic rock facies data obtained through waveform simulation. For example, the predicted volcanic rock lithology data obtained is as follows: Figure 5 As shown in Figure 2, the eruption phase volcanic rocks are close to the crater and have larger pores, which are favorable reservoirs in this area. Based on this data set, the plane distribution of the eruption phase volcanic rocks is predicted, see Figure 6 The correspondence with the drilled wells reached 95%. The volcanic rock lithofacies prediction method based on the seismic waveform indication provides strong support for the subsequent deployment of volcanic rock wells in the region.

[0083] In a specific embodiment, step S400 includes:

[0084] Step S410: obtaining a low-frequency model based on the acoustic wave impedance body according to the acoustic wave impedance body.

[0085] The low-frequency model uses calibrated acoustic wave impedance bodies for mathematical interpolation to establish a low-frequency model of the physical properties of underground media.

[0086] Step S420: According to the low-frequency model and the seismic data, waveform indication simulation is performed on the lithofacies indication curve data to obtain a predicted volcanic rock lithofacies data volume.

[0087] This embodiment uses the wave impedance body in the time domain as a low-frequency model, that is, the wave impedance body is used as a framework constraint of the lithofacies, and the lithofacies indicator curve is used to simulate the waveform indication of the seismic data according to the lateral change trend of the low-frequency model to obtain the volcanic rock lithofacies body, and then predicts the distribution of the volcanic rock lithofacies based on this data, thereby improving the accuracy of lithofacies prediction.

[0088] This embodiment obtains acoustic time difference curve data, gamma curve data, density curve data and post-stack seismic data, and then fits the acoustic time difference curve with the gamma curve to obtain a lithofacies indication curve. According to the acoustic time difference curve data and the density curve data, waveform indication inversion is performed on the post-stack seismic data to obtain an acoustic wave impedance body, and then waveform indication simulation is performed on the lithofacies indication curve based on the acoustic wave impedance body to obtain a predicted volcanic rock lithofacies data body. This embodiment matches and learns the well logging phase and the seismic phase, and uses the waveform indication method to establish a relationship between the well logging data in the depth domain and the seismic data in the time domain, thereby reducing the difficulty of identifying the volcanic rock lithology and improving the precision and accuracy of the volcanic rock lithology prediction.

[0089] Embodiment 2

[0090] Please refer to Figure 7 , Figure 7 The volcanic rock facies prediction device 200 provided in the embodiment of the present application includes:

[0091] The acquisition module 210 is used to acquire volcanic rock logging curve data and seismic data; wherein the logging curve data includes acoustic wave time difference curve data, gamma curve data and density curve data, and the seismic data includes post-stack seismic data;

[0092] A fitting module 220 is used to fit the acoustic time difference curve data and the gamma curve data to obtain the lithofacies indication curve data;

[0093] An inversion module 230 is used to perform waveform indication inversion on the post-stack seismic data according to the acoustic wave time difference curve data and the density curve data to obtain an acoustic wave impedance body;

[0094] The prediction module 240 is used to perform waveform indication simulation on the lithofacies indication curve data and the post-stack seismic data based on the constraints of the acoustic wave impedance body to obtain the predicted volcanic rock lithofacies data body.

[0095] It should be understood that the device corresponds to the above-mentioned volcanic rock facies prediction method embodiment, and can execute the various steps involved in the above-mentioned method embodiment. The specific functions of the device can be referred to in the above description. To avoid repetition, the detailed description is appropriately omitted here. The device includes at least one software function module that can be stored in a memory in the form of software or firmware or fixed in the operating system (OS) of the device.

[0096] Embodiment 3

[0097] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0098] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0099] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0100] Embodiment 4

[0101] An embodiment of the present invention further provides a computer-readable storage medium, on which instructions are stored, which are used to execute the program of each step described in any possible implementation of the method for predicting volcanic rock facies when executed by a processor.

[0102] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0103] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0106] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0107] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0108] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0109] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for predicting volcanic rock facies, characterized in that: include: Acquire volcanic rock logging curve data and seismic data; wherein the logging curve data includes acoustic wave time difference curve data, gamma curve data and density curve data, and the seismic data includes post-stack seismic data; Fitting the acoustic time difference curve data and the gamma curve data to obtain lithofacies indication curve data; According to the acoustic wave time difference curve data and the density curve data, waveform indication inversion is performed on the post-stack seismic data to obtain an acoustic wave impedance body; Based on the constraint of the acoustic wave impedance body, waveform indication simulation is performed on the lithofacies indication curve data and the post-stack seismic data to obtain a predicted volcanic rock lithofacies data body.

2. The method for predicting volcanic rock facies according to claim 1, characterized in that: The step of fitting the acoustic time difference curve data and the gamma curve data to obtain the lithofacies indication curve data includes: The lithofacies indication curve data is obtained according to the acoustic time difference curve data, the gamma curve data and the formula 1; wherein the formula 1 includes: litho=AC / GR, Wherein, litho is the lithofacies indication curve data, AC is the acoustic wave time difference curve data, and GR is the gamma curve data.

3. The method for predicting volcanic rock facies according to claim 1, characterized in that: Before performing waveform indication inversion on the post-stack seismic data according to the acoustic wave time difference curve data and the density curve data to obtain an acoustic wave impedance body, the method further comprises: Calculating an acoustic wave impedance curve according to the acoustic wave time difference curve data and the density curve data; According to the acoustic wave impedance curve, obtaining a reflection coefficient of the acoustic wave impedance curve; Synthetic seismic record data is obtained according to the reflection coefficient and the seismic data; wherein the seismic data includes seismic wavelet data.

4. The method for predicting volcanic rock facies according to claim 3, characterized in that: The synthesized seismic record data is obtained according to the reflection coefficient and the seismic data; wherein the seismic data includes seismic wavelet data, including: According to the reflection coefficient, the seismic wavelet data and the formula 2, synthetic seismic record data is obtained; wherein the formula 2 includes: s(t)=r(t)*w(t), Wherein, s(t) is the synthetic seismic record data, r(t) is the reflection coefficient, w(t) is the seismic wavelet data, and * is the convolution calculation.

5. The method for predicting volcanic rock facies according to claim 3, characterized in that: After obtaining synthetic seismic record data according to the reflection coefficient and the seismic data, the method further comprises: Determining whether the synthetic seismic record data is consistent with the time domain waveform of the post-stack earthquake; If not, the process returns to executing the step of calculating the acoustic wave impedance curve based on the acoustic wave time difference curve data and the density curve data.

6. The method for predicting volcanic rock facies according to claim 5, characterized in that: After determining whether the synthetic seismic record data is consistent with the time domain waveform of the post-stack seismic, the method further includes: If yes, then perform the waveform indication inversion on the post-stack seismic data according to the acoustic wave time difference curve data and the density curve data to obtain an acoustic wave impedance body; The method of performing waveform indication inversion on the post-stack seismic data according to the acoustic wave time difference curve data and the density curve data to obtain an acoustic wave impedance body comprises: If so, waveform inversion is performed on the post-stack seismic data according to the synthetic seismic record data to obtain the acoustic wave impedance body.

7. The method for predicting volcanic rock facies according to claim 1, characterized in that: The method of performing waveform indication simulation on the lithofacies indication curve data based on the acoustic wave impedance body to obtain a predicted volcanic rock lithofacies data body includes: According to the acoustic wave impedance body, a low-frequency model based on the acoustic wave impedance body is obtained; According to the low-frequency model and the seismic data, waveform indication simulation is performed on the lithofacies indication curve data to obtain the predicted volcanic rock lithofacies data.

8. A device for predicting volcanic rock facies, characterized in that: include: An acquisition module, used to acquire volcanic rock logging curve data and seismic data; wherein the logging curve data includes acoustic wave time difference curve data, gamma curve data and density curve data, and the seismic data includes post-stack seismic data; A fitting module, used for fitting the acoustic time difference curve data and the gamma curve data to obtain lithofacies indication curve data; An inversion module, used for performing waveform indication inversion on the post-stack seismic data according to the acoustic wave time difference curve data and the density curve data to obtain an acoustic wave impedance body; The prediction module is used to perform waveform indication simulation on the lithofacies indication curve data and the post-stack seismic data based on the constraints of the acoustic wave impedance body to obtain a predicted volcanic rock lithofacies data body.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the method for predicting volcanic rock facies according to any one of claims 1 to 7 is executed.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions for causing a machine to execute the method for predicting volcanic rock facies according to any one of claims 1 to 7.

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